Modeling and application of PLP-Net based on parallel location attention module
A parallel location attention module(PLAM)was designed to enhance the precise extraction of information from high-resolution remote sensing images and address the discrepancies in detailed information from using different satellites in multi-image source data.Based on this module,a new parallel location pyramid network(PLP-Net)was constructed by integrating contextual information in pyramid structures,connecting the network decoding layer with the deep network,and upsampling feature images to restore the size of the input image.Experimental comparisons and validations on a new test set revealed that the PLP-Net network achieved strong segmentation results,confirming its effectiveness in extracting complex feature details from remote sensing images.
deep learningremote sensing imagesemantic segmentationattention mechanism
万方数据
维普
